Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas

Abstract

Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam’s Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops.

Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information

“…Sentinel-2 images overcome some of these limitations and showed its potential in agricultural applications [14,15], however in areas with high spatial fragmentation they may be not enough [16,17]. The Sentinel-2 satellite images did not show enough accuracy to identify abandoned plots in our study area due to resolution limitations and the small size of the plots, for this reason, the use of higher resolution images is recommended [18].…”

“…The detection of abandoned plots is based on the spectral and textural differences caused by the growth of wild vegetation, the loss of vigor of the trees (decreased foliar density and greenness) and the loss of regular spatial patterns of the crop. A more detailed description of the ecological succession of an abandoned plot can be found in Morell et al, 2020 [18].…”

“…The image resulting from the pixel-based classification was segmented using the cadastral database. Then, these segments were used to enhance the classification results of the pixel based Random Forests algorithm using the majority voting within each segment [18]. This procedure creates an object-based map where each object is a cadastral plot [50] and improves the accuracy of pixel-based classification by removing misclassified pixels, within homogeneous segments, known as salt-and-pepper noise [51].…”

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

“…(1) ALS data allowed for an automated and more accurate identification of AAL in terms of classification accuracy (>90%) and spatial resolution (<1.0 m) than did other RS platforms [53][54][55][56][57][58][59][60][61][62][63][64]. Potential improvements in process of AAL identification may be achieved using some qualitative variable of ALS data (e.g., intensity) or alternatively through multispectral ALS data [65][66][67][68].…”

“…This is because they are generally more accessible for large areas than ALS data. Here, an application of spectral or multiscale features extracted from aerial [60] or satellite images, such as GF-2 [61], Quick Bird [62], and Landsat [63], resulted in an overall accuracy of 77-91%. Several studies have also demonstrated the potential of radar data as an alternative to optical images for the identification of AAL.…”